Overview

Dataset statistics

Number of variables52
Number of observations13378
Missing cells528
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.6 MiB
Average record size in memory2.4 KiB

Variable types

CAT39
NUM12
BOOL1

Warnings

ano has constant value "13378" Constant
hogar has constant value "13378" Constant
p4 has constant value "13378" Constant
icvmd is highly correlated with p10High correlation
p10 is highly correlated with icvmdHigh correlation
p6 is highly correlated with p5High correlation
p5 is highly correlated with p6High correlation
p162 is highly correlated with p149 and 2 other fieldsHigh correlation
p149 is highly correlated with p162High correlation
p163 is highly correlated with p162High correlation
p164 is highly correlated with p162High correlation
p166 is highly correlated with p165High correlation
p165 is highly correlated with p166High correlation
p168 is highly correlated with p167High correlation
p167 is highly correlated with p168 and 2 other fieldsHigh correlation
p169 is highly correlated with p167High correlation
p170 is highly correlated with p167High correlation
p172 is highly correlated with p171High correlation
p171 is highly correlated with p172 and 1 other fieldsHigh correlation
p173 is highly correlated with p171High correlation
p175 is highly correlated with p174High correlation
p174 is highly correlated with p175High correlation
p177 is highly correlated with p176High correlation
p176 is highly correlated with p177High correlation
p179 is highly correlated with p178High correlation
p178 is highly correlated with p179 and 2 other fieldsHigh correlation
p180 is highly correlated with p178High correlation
p181 is highly correlated with p178High correlation
icvmd has 268 (2.0%) missing values Missing
icv has 251 (1.9%) missing values Missing
form has unique values Unique
p150 has 7111 (53.2%) zeros Zeros
p154 has 11488 (85.9%) zeros Zeros
p155 has 367 (2.7%) zeros Zeros
p156 has 9963 (74.5%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:51:45.065244
Analysis finished2020-12-13 00:52:20.417165
Duration35.35 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ano
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
2013
13378 
ValueCountFrequency (%) 
201313378100.0%
 
2020-12-12T19:52:20.489728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:20.536268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:20.580807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
21337825.0%
 
01337825.0%
 
11337825.0%
 
31337825.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number53512100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
21337825.0%
 
01337825.0%
 
11337825.0%
 
31337825.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common53512100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
21337825.0%
 
01337825.0%
 
11337825.0%
 
31337825.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53512100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
21337825.0%
 
01337825.0%
 
11337825.0%
 
31337825.0%
 

form
Real number (ℝ≥0)

UNIQUE

Distinct13378
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48305.5
Minimum41617
Maximum54994
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:20.666380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41617
5-th percentile42285.85
Q144961.25
median48305.5
Q351649.75
95-th percentile54325.15
Maximum54994
Range13377
Interquartile range (IQR)6688.5

Descriptive statistics

Standard deviation3862.040285
Coefficient of variation (CV)0.07995032213
Kurtosis-1.2
Mean48305.5
Median Absolute Deviation (MAD)3344.5
Skewness0
Sum646230979
Variance14915355.17
MonotocityNot monotonic
2020-12-12T19:52:20.758460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
430031< 0.1%
 
499651< 0.1%
 
499011< 0.1%
 
519481< 0.1%
 
457951< 0.1%
 
478421< 0.1%
 
416971< 0.1%
 
437441< 0.1%
 
539831< 0.1%
 
498851< 0.1%
 
519321< 0.1%
 
457791< 0.1%
 
478261< 0.1%
 
416811< 0.1%
 
437281< 0.1%
 
539671< 0.1%
 
498691< 0.1%
 
519161< 0.1%
 
457631< 0.1%
 
478101< 0.1%
 
416651< 0.1%
 
437121< 0.1%
 
539511< 0.1%
 
498531< 0.1%
 
519001< 0.1%
 
Other values (13353)1335399.8%
 
ValueCountFrequency (%) 
416171< 0.1%
 
416181< 0.1%
 
416191< 0.1%
 
416201< 0.1%
 
416211< 0.1%
 
416221< 0.1%
 
416231< 0.1%
 
416241< 0.1%
 
416251< 0.1%
 
416261< 0.1%
 
ValueCountFrequency (%) 
549941< 0.1%
 
549931< 0.1%
 
549921< 0.1%
 
549911< 0.1%
 
549901< 0.1%
 
549891< 0.1%
 
549881< 0.1%
 
549871< 0.1%
 
549861< 0.1%
 
549851< 0.1%
 

hogar
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
1
13378 
ValueCountFrequency (%) 
113378100.0%
 
2020-12-12T19:52:20.820513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

p1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
7091 
No
6287 
ValueCountFrequency (%) 
709153.0%
 
No628747.0%
 
2020-12-12T19:52:20.870556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:20.918597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:20.966138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S709126.5%
 
í709126.5%
 
N628723.5%
 
o628723.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S709153.0%
 
N628747.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
í709153.0%
 
o628747.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S709126.5%
 
í709126.5%
 
N628723.5%
 
o628723.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1966573.5%
 
None709126.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S709136.1%
 
N628732.0%
 
o628732.0%
 

Most frequent None characters

ValueCountFrequency (%) 
í7091100.0%
 

p4
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Medellín
13378 
ValueCountFrequency (%) 
Medellín13378100.0%
 
2020-12-12T19:52:21.033196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:21.075732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:21.119771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e2675625.0%
 
l2675625.0%
 
M1337812.5%
 
d1337812.5%
 
í1337812.5%
 
n1337812.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter9364687.5%
 
Uppercase Letter1337812.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M13378100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e2675628.6%
 
l2675628.6%
 
d1337814.3%
 
í1337814.3%
 
n1337814.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin107024100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e2675625.0%
 
l2675625.0%
 
M1337812.5%
 
d1337812.5%
 
í1337812.5%
 
n1337812.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9364687.5%
 
None1337812.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e2675628.6%
 
l2675628.6%
 
M1337814.3%
 
d1337814.3%
 
n1337814.3%
 

Most frequent None characters

ValueCountFrequency (%) 
í13378100.0%
 

p5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Urbano
12194 
Rural
 
1184
ValueCountFrequency (%) 
Urbano1219491.1%
 
Rural11848.9%
 
2020-12-12T19:52:21.187329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:21.230866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:21.279408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.911496487
Min length5

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r1337816.9%
 
a1337816.9%
 
U1219415.4%
 
b1219415.4%
 
n1219415.4%
 
o1219415.4%
 
R11841.5%
 
u11841.5%
 
l11841.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6570683.1%
 
Uppercase Letter1337816.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U1219491.1%
 
R11848.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r1337820.4%
 
a1337820.4%
 
b1219418.6%
 
n1219418.6%
 
o1219418.6%
 
u11841.8%
 
l11841.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin79084100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r1337816.9%
 
a1337816.9%
 
U1219415.4%
 
b1219415.4%
 
n1219415.4%
 
o1219415.4%
 
R11841.5%
 
u11841.5%
 
l11841.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII79084100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r1337816.9%
 
a1337816.9%
 
U1219415.4%
 
b1219415.4%
 
n1219415.4%
 
o1219415.4%
 
R11841.5%
 
u11841.5%
 
l11841.5%
 

p6
Categorical

HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
BELEN
1257 
ROBLEDO
941 
SAN JAVIER
885 
DOCE DE OCTUBRE
871 
MANRIQUE
865 
Other values (16)
8559 
ValueCountFrequency (%) 
BELEN12579.4%
 
ROBLEDO9417.0%
 
SAN JAVIER8856.6%
 
DOCE DE OCTUBRE8716.5%
 
MANRIQUE8656.5%
 
BUENOS AIRES8406.3%
 
VILLA HERMOSA8296.2%
 
EL POBLADO7695.7%
 
ARANJUEZ7675.7%
 
LAURELES-ESTADIO7375.5%
 
POPULAR6785.1%
 
CASTILLA6675.0%
 
LA AMERICA6224.6%
 
SANTA CRUZ5534.1%
 
LA CANDELARIA5434.1%
 
SAN ANTONIO DE PRADO4753.6%
 
SAN CRISTOBAL4303.2%
 
GUAYABAL3702.8%
 
ALTAVISTA1250.9%
 
SANTA ELENA860.6%
 
PALMITAS680.5%
 
2020-12-12T19:52:21.355474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:21.432540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length10
Mean length10.29585887
Min length5

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A2023014.7%
 
E1588611.5%
 
L116678.5%
 
O101017.3%
 
R100367.3%
 
87246.3%
 
N77375.6%
 
S77025.6%
 
I70865.1%
 
D56824.1%
 
U56814.1%
 
B54784.0%
 
C45573.3%
 
T41373.0%
 
P26681.9%
 
M23841.7%
 
V18391.3%
 
J16521.2%
 
Z13201.0%
 
Q8650.6%
 
H8290.6%
 
-7370.5%
 
G3700.3%
 
Y3700.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter12827793.1%
 
Space Separator87246.3%
 
Dash Punctuation7370.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2023015.8%
 
E1588612.4%
 
L116679.1%
 
O101017.9%
 
R100367.8%
 
N77376.0%
 
S77026.0%
 
I70865.5%
 
D56824.4%
 
U56814.4%
 
B54784.3%
 
C45573.6%
 
T41373.2%
 
P26682.1%
 
M23841.9%
 
V18391.4%
 
J16521.3%
 
Z13201.0%
 
Q8650.7%
 
H8290.6%
 
G3700.3%
 
Y3700.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
8724100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-737100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12827793.1%
 
Common94616.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2023015.8%
 
E1588612.4%
 
L116679.1%
 
O101017.9%
 
R100367.8%
 
N77376.0%
 
S77026.0%
 
I70865.5%
 
D56824.4%
 
U56814.4%
 
B54784.3%
 
C45573.6%
 
T41373.2%
 
P26682.1%
 
M23841.9%
 
V18391.4%
 
J16521.3%
 
Z13201.0%
 
Q8650.7%
 
H8290.6%
 
G3700.3%
 
Y3700.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
872492.2%
 
-7377.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII137738100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A2023014.7%
 
E1588611.5%
 
L116678.5%
 
O101017.3%
 
R100367.3%
 
87246.3%
 
N77375.6%
 
S77025.6%
 
I70865.1%
 
D56824.1%
 
U56814.1%
 
B54784.0%
 
C45573.3%
 
T41373.0%
 
P26681.9%
 
M23841.7%
 
V18391.3%
 
J16521.2%
 
Z13201.0%
 
Q8650.6%
 
H8290.6%
 
-7370.5%
 
G3700.3%
 
Y3700.3%
 

p7
Real number (ℝ≥0)

Distinct284
Distinct (%)2.1%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1013968.334
Minimum1001001
Maximum1090011
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:21.517613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1001001
5-th percentile1001012
Q11005011
median1009011
Q31014009
95-th percentile1060098
Maximum1090011
Range89010
Interquartile range (IQR)8998

Descriptive statistics

Standard deviation18089.47299
Coefficient of variation (CV)0.01784027409
Kurtosis6.961779639
Mean1013968.334
Median Absolute Deviation (MAD)4007
Skewness2.808340711
Sum1.355574265e+10
Variance327229033
MonotocityNot monotonic
2020-12-12T19:52:21.604187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10800004263.2%
 
10600982011.5%
 
10030021561.2%
 
10060071481.1%
 
10040051471.1%
 
10090131451.1%
 
10160101421.1%
 
10010011260.9%
 
10160051250.9%
 
10160111240.9%
 
10010031230.9%
 
10050111230.9%
 
10070121230.9%
 
10070131150.9%
 
10060051150.9%
 
10150111130.8%
 
10120131120.8%
 
10060041110.8%
 
10100161080.8%
 
10110081060.8%
 
1008007980.7%
 
1003003960.7%
 
1003009950.7%
 
1009003900.7%
 
1003001900.7%
 
Other values (259)1001174.8%
 
ValueCountFrequency (%) 
10010011260.9%
 
1001002400.3%
 
10010031230.9%
 
1001004600.4%
 
1001005770.6%
 
1001006630.5%
 
1001007630.5%
 
1001008190.1%
 
1001009130.1%
 
1001010170.1%
 
ValueCountFrequency (%) 
10900113< 0.1%
 
10900104< 0.1%
 
109000970.1%
 
10900055< 0.1%
 
1090004190.1%
 
1090003210.2%
 
10900024< 0.1%
 
1080098160.1%
 
1080007130.1%
 
10800052< 0.1%
 

p10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.801166094
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:21.680753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.274739538
Coefficient of variation (CV)0.4550745993
Kurtosis0.03978914724
Mean2.801166094
Median Absolute Deviation (MAD)1
Skewness0.742009783
Sum37474
Variance1.624960889
MonotocityNot monotonic
2020-12-12T19:52:21.740805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
2468135.0%
 
3389929.1%
 
1166512.4%
 
4148411.1%
 
510808.1%
 
65694.3%
 
ValueCountFrequency (%) 
1166512.4%
 
2468135.0%
 
3389929.1%
 
4148411.1%
 
510808.1%
 
65694.3%
 
ValueCountFrequency (%) 
65694.3%
 
510808.1%
 
4148411.1%
 
3389929.1%
 
2468135.0%
 
1166512.4%
 

p11
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
1
13340 
2
 
35
3
 
3
ValueCountFrequency (%) 
11334099.7%
 
2350.3%
 
33< 0.1%
 
2020-12-12T19:52:21.812867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:21.863911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:21.913954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11334099.7%
 
2350.3%
 
33< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13378100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11334099.7%
 
2350.3%
 
33< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common13378100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11334099.7%
 
2350.3%
 
33< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13378100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11334099.7%
 
2350.3%
 
33< 0.1%
 

p146
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Apartamento
9359 
Casa
3900 
Cuarto(s)
 
83
Rancho o vivienda de desechos
 
32
Cuarto en Inquilinato
 
4
ValueCountFrequency (%) 
Apartamento935970.0%
 
Casa390029.2%
 
Cuarto(s)830.6%
 
Rancho o vivienda de desechos320.2%
 
Cuarto en Inquilinato4< 0.1%
 
2020-12-12T19:52:21.982013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:22.027051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:22.089105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length11
Mean length8.992973539
Min length4

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a2667322.2%
 
t1880915.6%
 
o95467.9%
 
e94917.9%
 
r94467.9%
 
n94357.8%
 
A93597.8%
 
p93597.8%
 
m93597.8%
 
s40473.4%
 
C39873.3%
 
1360.1%
 
d960.1%
 
u910.1%
 
(830.1%
 
)830.1%
 
i720.1%
 
c640.1%
 
h640.1%
 
v640.1%
 
R32< 0.1%
 
I4< 0.1%
 
q4< 0.1%
 
l4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10662488.6%
 
Uppercase Letter1338211.1%
 
Space Separator1360.1%
 
Open Punctuation830.1%
 
Close Punctuation830.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A935969.9%
 
C398729.8%
 
R320.2%
 
I4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a2667325.0%
 
t1880917.6%
 
o95469.0%
 
e94918.9%
 
r94468.9%
 
n94358.8%
 
p93598.8%
 
m93598.8%
 
s40473.8%
 
d960.1%
 
u910.1%
 
i720.1%
 
c640.1%
 
h640.1%
 
v640.1%
 
q4< 0.1%
 
l4< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(83100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)83100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
136100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12000699.7%
 
Common3020.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a2667322.2%
 
t1880915.7%
 
o95468.0%
 
e94917.9%
 
r94467.9%
 
n94357.9%
 
A93597.8%
 
p93597.8%
 
m93597.8%
 
s40473.4%
 
C39873.3%
 
d960.1%
 
u910.1%
 
i720.1%
 
c640.1%
 
h640.1%
 
v640.1%
 
R32< 0.1%
 
I4< 0.1%
 
q4< 0.1%
 
l4< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
13645.0%
 
(8327.5%
 
)8327.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII120308100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a2667322.2%
 
t1880915.6%
 
o95467.9%
 
e94917.9%
 
r94467.9%
 
n94357.8%
 
A93597.8%
 
p93597.8%
 
m93597.8%
 
s40473.4%
 
C39873.3%
 
1360.1%
 
d960.1%
 
u910.1%
 
(830.1%
 
)830.1%
 
i720.1%
 
c640.1%
 
h640.1%
 
v640.1%
 
R32< 0.1%
 
I4< 0.1%
 
q4< 0.1%
 
l4< 0.1%
 

p147
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Ladrillo-bloque-adobe revocado o o pintado
9346 
Ladrillo-bloque-adobe sin ranurar_ sin revocar o sin revitar
1436 
Ladrillo ranurado o revitado
1206 
Ladrillo-bloque forrado en piedra o madera
 
551
Bloque ranurado o revitado
 
531
Other values (6)
 
308
ValueCountFrequency (%) 
Ladrillo-bloque-adobe revocado o o pintado934669.9%
 
Ladrillo-bloque-adobe sin ranurar_ sin revocar o sin revitar143610.7%
 
Ladrillo ranurado o revitado12069.0%
 
Ladrillo-bloque forrado en piedra o madera5514.1%
 
Bloque ranurado o revitado5314.0%
 
Madera burda1401.0%
 
Material prefabricado_ Dry-wall800.6%
 
Tapia pisada520.4%
 
Bahareque sin revocar_ guadua_ caña_ esterilla_ otro vegetal150.1%
 
Bahareque revocado110.1%
 
Materiales de desechos y otros (zinc_ tela_ lona_ cartón_ latas_ desechos plásticos_ etc)100.1%
 
2020-12-12T19:52:22.161167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:22.233729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length89
Median length42
Mean length41.57422634
Min length12

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9185816.5%
 
a5643810.1%
 
5599010.1%
 
d476088.6%
 
r400797.2%
 
e387827.0%
 
l372776.7%
 
i302415.4%
 
b223354.0%
 
-221954.0%
 
n174233.1%
 
u152332.7%
 
v139962.5%
 
t127142.3%
 
L125392.3%
 
q118902.1%
 
c109632.0%
 
p100911.8%
 
s44800.8%
 
_16360.3%
 
f6310.1%
 
B5570.1%
 
m5510.1%
 
M230< 0.1%
 
y90< 0.1%
 
Other values (11)3530.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter46288183.2%
 
Space Separator5599010.1%
 
Dash Punctuation221954.0%
 
Uppercase Letter134582.4%
 
Connector Punctuation16360.3%
 
Open Punctuation10< 0.1%
 
Close Punctuation10< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L1253993.2%
 
B5574.1%
 
M2301.7%
 
D800.6%
 
T520.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9185819.8%
 
a5643812.2%
 
d4760810.3%
 
r400798.7%
 
e387828.4%
 
l372778.1%
 
i302416.5%
 
b223354.8%
 
n174233.8%
 
u152333.3%
 
v139963.0%
 
t127142.7%
 
q118902.6%
 
c109632.4%
 
p100912.2%
 
s44801.0%
 
f6310.1%
 
m5510.1%
 
y90< 0.1%
 
w80< 0.1%
 
h46< 0.1%
 
g30< 0.1%
 
ñ15< 0.1%
 
z10< 0.1%
 
ó10< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-22195100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
55990100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1636100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47633985.6%
 
Common7984114.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9185819.3%
 
a5643811.8%
 
d4760810.0%
 
r400798.4%
 
e387828.1%
 
l372777.8%
 
i302416.3%
 
b223354.7%
 
n174233.7%
 
u152333.2%
 
v139962.9%
 
t127142.7%
 
L125392.6%
 
q118902.5%
 
c109632.3%
 
p100912.1%
 
s44800.9%
 
f6310.1%
 
B5570.1%
 
m5510.1%
 
M230< 0.1%
 
y90< 0.1%
 
D80< 0.1%
 
w80< 0.1%
 
T52< 0.1%
 
Other values (6)121< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
5599070.1%
 
-2219527.8%
 
_16362.0%
 
(10< 0.1%
 
)10< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII556145> 99.9%
 
None35< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9185816.5%
 
a5643810.1%
 
5599010.1%
 
d476088.6%
 
r400797.2%
 
e387827.0%
 
l372776.7%
 
i302415.4%
 
b223354.0%
 
-221954.0%
 
n174233.1%
 
u152332.7%
 
v139962.5%
 
t127142.3%
 
L125392.3%
 
q118902.1%
 
c109632.0%
 
p100911.8%
 
s44800.8%
 
_16360.3%
 
f6310.1%
 
B5570.1%
 
m5510.1%
 
M230< 0.1%
 
y90< 0.1%
 
Other values (8)3180.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ1542.9%
 
ó1028.6%
 
á1028.6%
 

p148
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Baldosa_ vinilo_ tableta o ladrillo
10006 
Cemento o gravilla
2922 
Mármol
 
196
Madera pulida y lacada_ parqué_ cristal
 
144
Madera burda_ tabla_ tablón u otro vegetal
 
57
Other values (2)
 
53
ValueCountFrequency (%) 
Baldosa_ vinilo_ tableta o ladrillo1000674.8%
 
Cemento o gravilla292221.8%
 
Mármol1961.5%
 
Madera pulida y lacada_ parqué_ cristal1441.1%
 
Madera burda_ tabla_ tablón u otro vegetal570.4%
 
Tierra o arena510.4%
 
Alfombra o tapete de pared a pared2< 0.1%
 
2020-12-12T19:52:22.309294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:22.356335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:22.435903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length35
Mean length30.8546868
Min length6

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l6668116.2%
 
a5758814.0%
 
4704411.4%
 
o4623311.2%
 
i332798.1%
 
t233105.6%
 
d205645.0%
 
_204144.9%
 
e162773.9%
 
r138863.4%
 
n130363.2%
 
v129853.1%
 
b101792.5%
 
s101502.5%
 
B100062.4%
 
m31200.8%
 
g29790.7%
 
C29220.7%
 
u4020.1%
 
M3970.1%
 
p2940.1%
 
c2880.1%
 
á196< 0.1%
 
y144< 0.1%
 
q144< 0.1%
 
Other values (5)2560.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter33193880.4%
 
Space Separator4704411.4%
 
Connector Punctuation204144.9%
 
Uppercase Letter133783.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1000674.8%
 
C292221.8%
 
M3973.0%
 
T510.4%
 
A2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l6668120.1%
 
a5758817.3%
 
o4623313.9%
 
i3327910.0%
 
t233107.0%
 
d205646.2%
 
e162774.9%
 
r138864.2%
 
n130363.9%
 
v129853.9%
 
b101793.1%
 
s101503.1%
 
m31200.9%
 
g29790.9%
 
u4020.1%
 
p2940.1%
 
c2880.1%
 
á1960.1%
 
y144< 0.1%
 
q144< 0.1%
 
é144< 0.1%
 
ó57< 0.1%
 
f2< 0.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_20414100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
47044100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34531683.7%
 
Common6745816.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l6668119.3%
 
a5758816.7%
 
o4623313.4%
 
i332799.6%
 
t233106.8%
 
d205646.0%
 
e162774.7%
 
r138864.0%
 
n130363.8%
 
v129853.8%
 
b101792.9%
 
s101502.9%
 
B100062.9%
 
m31200.9%
 
g29790.9%
 
C29220.8%
 
u4020.1%
 
M3970.1%
 
p2940.1%
 
c2880.1%
 
á1960.1%
 
y144< 0.1%
 
q144< 0.1%
 
é144< 0.1%
 
ó57< 0.1%
 
Other values (3)55< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
4704469.7%
 
_2041430.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII41237799.9%
 
None3970.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l6668116.2%
 
a5758814.0%
 
4704411.4%
 
o4623311.2%
 
i332798.1%
 
t233105.7%
 
d205645.0%
 
_204145.0%
 
e162773.9%
 
r138863.4%
 
n130363.2%
 
v129853.1%
 
b101792.5%
 
s101502.5%
 
B100062.4%
 
m31200.8%
 
g29790.7%
 
C29220.7%
 
u4020.1%
 
M3970.1%
 
p2940.1%
 
c2880.1%
 
y144< 0.1%
 
q144< 0.1%
 
T51< 0.1%
 
Other values (2)4< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
á19649.4%
 
é14436.3%
 
ó5714.4%
 

p149
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Entidad prestadora de servicios públicos
12911 
Acueducto veredal
 
276
Pila pública
 
103
Río_ quebrada_ manantial o nacimiento
 
48
Pozo con bomba
 
27
Other values (5)
 
13
ValueCountFrequency (%) 
Entidad prestadora de servicios públicos1291196.5%
 
Acueducto veredal2762.1%
 
Pila pública1030.8%
 
Río_ quebrada_ manantial o nacimiento480.4%
 
Pozo con bomba270.2%
 
Pozo sin bomba_ aljibe_ jagüey o barreno4< 0.1%
 
Aguatero4< 0.1%
 
Carro tanque2< 0.1%
 
Agua embotellada o en bolsa2< 0.1%
 
Aguas lluvias1< 0.1%
 
2020-12-12T19:52:22.512969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T19:52:22.564514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:22.657093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length40
Mean length39.22895799
Min length8

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
5230410.0%
 
d5224610.0%
 
i520039.9%
 
s516529.8%
 
e396817.6%
 
a395647.5%
 
o392937.5%
 
r390737.4%
 
c265525.1%
 
t262025.0%
 
p259254.9%
 
l134532.6%
 
v131882.5%
 
n131422.5%
 
b131362.5%
 
ú130142.5%
 
E129112.5%
 
u6100.1%
 
A2830.1%
 
P134< 0.1%
 
m129< 0.1%
 
_104< 0.1%
 
q50< 0.1%
 
R48< 0.1%
 
í48< 0.1%
 
Other values (6)60< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter45901987.5%
 
Space Separator5230410.0%
 
Uppercase Letter133782.5%
 
Connector Punctuation104< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E1291196.5%
 
A2832.1%
 
P1341.0%
 
R480.4%
 
C2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d5224611.4%
 
i5200311.3%
 
s5165211.3%
 
e396818.6%
 
a395648.6%
 
o392938.6%
 
r390738.5%
 
c265525.8%
 
t262025.7%
 
p259255.6%
 
l134532.9%
 
v131882.9%
 
n131422.9%
 
b131362.9%
 
ú130142.8%
 
u6100.1%
 
m129< 0.1%
 
q50< 0.1%
 
í48< 0.1%
 
z31< 0.1%
 
g11< 0.1%
 
j8< 0.1%
 
ü4< 0.1%
 
y4< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
52304100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_104100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47239790.0%
 
Common5240810.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d5224611.1%
 
i5200311.0%
 
s5165210.9%
 
e396818.4%
 
a395648.4%
 
o392938.3%
 
r390738.3%
 
c265525.6%
 
t262025.5%
 
p259255.5%
 
l134532.8%
 
v131882.8%
 
n131422.8%
 
b131362.8%
 
ú130142.8%
 
E129112.7%
 
u6100.1%
 
A2830.1%
 
P134< 0.1%
 
m129< 0.1%
 
q50< 0.1%
 
R48< 0.1%
 
í48< 0.1%
 
z31< 0.1%
 
g11< 0.1%
 
Other values (4)18< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
5230499.8%
 
_1040.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII51173997.5%
 
None130662.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
5230410.2%
 
d5224610.2%
 
i5200310.2%
 
s5165210.1%
 
e396817.8%
 
a395647.7%
 
o392937.7%
 
r390737.6%
 
c265525.2%
 
t262025.1%
 
p259255.1%
 
l134532.6%
 
v131882.6%
 
n131422.6%
 
b131362.6%
 
E129112.5%
 
u6100.1%
 
A2830.1%
 
P134< 0.1%
 
m129< 0.1%
 
_104< 0.1%
 
q50< 0.1%
 
R48< 0.1%
 
z31< 0.1%
 
g11< 0.1%
 
Other values (3)14< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ú1301499.6%
 
í480.4%
 
ü4< 0.1%
 

p150
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4761548811
Minimum0
Maximum4
Zeros7111
Zeros (%)53.2%
Memory size104.6 KiB
2020-12-12T19:52:22.722650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5169543391
Coefficient of variation (CV)1.085685267
Kurtosis-0.99895351
Mean0.4761548811
Median Absolute Deviation (MAD)0
Skewness0.3437435872
Sum6370
Variance0.2672417887
MonotocityNot monotonic
2020-12-12T19:52:22.784703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0711153.2%
 
1617946.2%
 
2740.6%
 
3130.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
0711153.2%
 
1617946.2%
 
2740.6%
 
3130.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
41< 0.1%
 
3130.1%
 
2740.6%
 
1617946.2%
 
0711153.2%
 

p151
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
0
10157 
1
3201 
2
 
20
ValueCountFrequency (%) 
01015775.9%
 
1320123.9%
 
2200.1%
 
2020-12-12T19:52:22.862770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:22.909811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:22.960355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01015775.9%
 
1320123.9%
 
2200.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13378100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01015775.9%
 
1320123.9%
 
2200.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common13378100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
01015775.9%
 
1320123.9%
 
2200.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13378100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01015775.9%
 
1320123.9%
 
2200.1%
 

p152
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
0
7593 
1
5769 
2
 
16
ValueCountFrequency (%) 
0759356.8%
 
1576943.1%
 
2160.1%
 
2020-12-12T19:52:23.033918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:23.083961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:23.134004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0759356.8%
 
1576943.1%
 
2160.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13378100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0759356.8%
 
1576943.1%
 
2160.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common13378100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0759356.8%
 
1576943.1%
 
2160.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13378100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0759356.8%
 
1576943.1%
 
2160.1%
 

p153
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
1
13169 
0
 
184
2
 
25
ValueCountFrequency (%) 
11316998.4%
 
01841.4%
 
2250.2%
 
2020-12-12T19:52:23.206066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:23.253607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:23.304650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11316998.4%
 
01841.4%
 
2250.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13378100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11316998.4%
 
01841.4%
 
2250.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common13378100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11316998.4%
 
01841.4%
 
2250.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13378100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11316998.4%
 
01841.4%
 
2250.2%
 

p154
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1696068172
Minimum0
Maximum6
Zeros11488
Zeros (%)85.9%
Memory size104.6 KiB
2020-12-12T19:52:23.364702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4656201595
Coefficient of variation (CV)2.745291535
Kurtosis17.14933509
Mean0.1696068172
Median Absolute Deviation (MAD)0
Skewness3.53441399
Sum2269
Variance0.2168021329
MonotocityNot monotonic
2020-12-12T19:52:23.421751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
01148885.9%
 
1160912.0%
 
22091.6%
 
3500.4%
 
4190.1%
 
52< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
01148885.9%
 
1160912.0%
 
22091.6%
 
3500.4%
 
4190.1%
 
52< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
52< 0.1%
 
4190.1%
 
3500.4%
 
22091.6%
 
1160912.0%
 
01148885.9%
 

p155
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.57811332
Minimum0
Maximum10
Zeros367
Zeros (%)2.7%
Memory size104.6 KiB
2020-12-12T19:52:23.488309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.064309914
Coefficient of variation (CV)0.4128251097
Kurtosis2.108026595
Mean2.57811332
Median Absolute Deviation (MAD)1
Skewness0.3248973489
Sum34490
Variance1.132755594
MonotocityNot monotonic
2020-12-12T19:52:23.550863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
3540840.4%
 
2422131.6%
 
4154611.6%
 
1143010.7%
 
03672.7%
 
52882.2%
 
6850.6%
 
7180.1%
 
86< 0.1%
 
105< 0.1%
 
94< 0.1%
 
ValueCountFrequency (%) 
03672.7%
 
1143010.7%
 
2422131.6%
 
3540840.4%
 
4154611.6%
 
52882.2%
 
6850.6%
 
7180.1%
 
86< 0.1%
 
94< 0.1%
 
ValueCountFrequency (%) 
105< 0.1%
 
94< 0.1%
 
86< 0.1%
 
7180.1%
 
6850.6%
 
52882.2%
 
4154611.6%
 
3540840.4%
 
2422131.6%
 
1143010.7%
 

p156
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3092390492
Minimum0
Maximum10
Zeros9963
Zeros (%)74.5%
Memory size104.6 KiB
2020-12-12T19:52:23.614417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6181621119
Coefficient of variation (CV)1.998978181
Kurtosis18.93013017
Mean0.3092390492
Median Absolute Deviation (MAD)0
Skewness3.152901334
Sum4137
Variance0.3821243966
MonotocityNot monotonic
2020-12-12T19:52:23.677471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0996374.5%
 
1294022.0%
 
23152.4%
 
31080.8%
 
4350.3%
 
590.1%
 
63< 0.1%
 
72< 0.1%
 
82< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
0996374.5%
 
1294022.0%
 
23152.4%
 
31080.8%
 
4350.3%
 
590.1%
 
63< 0.1%
 
72< 0.1%
 
82< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
82< 0.1%
 
72< 0.1%
 
63< 0.1%
 
590.1%
 
4350.3%
 
31080.8%
 
23152.4%
 
1294022.0%
 
0996374.5%
 

p157
Real number (ℝ≥0)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.197114666
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:23.740526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile8
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.721665795
Coefficient of variation (CV)0.3312733903
Kurtosis2.18619075
Mean5.197114666
Median Absolute Deviation (MAD)1
Skewness0.7687689841
Sum69527
Variance2.964133111
MonotocityNot monotonic
2020-12-12T19:52:23.804581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
5370627.7%
 
4304022.7%
 
6247818.5%
 
713099.8%
 
311178.3%
 
86875.1%
 
24003.0%
 
92882.2%
 
11321.0%
 
101100.8%
 
11530.4%
 
12300.2%
 
13160.1%
 
154< 0.1%
 
143< 0.1%
 
163< 0.1%
 
172< 0.1%
 
ValueCountFrequency (%) 
11321.0%
 
24003.0%
 
311178.3%
 
4304022.7%
 
5370627.7%
 
6247818.5%
 
713099.8%
 
86875.1%
 
92882.2%
 
101100.8%
 
ValueCountFrequency (%) 
172< 0.1%
 
163< 0.1%
 
154< 0.1%
 
143< 0.1%
 
13160.1%
 
12300.2%
 
11530.4%
 
101100.8%
 
92882.2%
 
86875.1%
 

p158
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
13358 
No
 
19
0
 
1
ValueCountFrequency (%) 
Si1335899.9%
 
No190.1%
 
01< 0.1%
 
2020-12-12T19:52:23.885651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T19:52:23.935193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:23.986738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.99992525
Min length1

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S1335849.9%
 
i1335849.9%
 
N190.1%
 
o190.1%
 
01< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337750.0%
 
Lowercase Letter1337750.0%
 
Decimal Number1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1335899.9%
 
N190.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1335899.9%
 
o190.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26754> 99.9%
 
Common1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1335849.9%
 
i1335849.9%
 
N190.1%
 
o190.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
01100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26755100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S1335849.9%
 
i1335849.9%
 
N190.1%
 
o190.1%
 
01< 0.1%
 

p159
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
13095 
Regular
 
190
Malo
 
74
No aplica
 
19
ValueCountFrequency (%) 
Bueno1309597.9%
 
Regular1901.4%
 
Malo740.6%
 
No aplica190.1%
 
2020-12-12T19:52:24.059300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.106341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:24.162889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.028554343
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
u1328519.7%
 
e1328519.7%
 
o1318819.6%
 
B1309519.5%
 
n1309519.5%
 
a3020.4%
 
l2830.4%
 
R1900.3%
 
g1900.3%
 
r1900.3%
 
M740.1%
 
N19< 0.1%
 
19< 0.1%
 
p19< 0.1%
 
i19< 0.1%
 
c19< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5387580.1%
 
Uppercase Letter1337819.9%
 
Space Separator19< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1309597.9%
 
R1901.4%
 
M740.6%
 
N190.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u1328524.7%
 
e1328524.7%
 
o1318824.5%
 
n1309524.3%
 
a3020.6%
 
l2830.5%
 
g1900.4%
 
r1900.4%
 
p19< 0.1%
 
i19< 0.1%
 
c19< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin67253> 99.9%
 
Common19< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u1328519.8%
 
e1328519.8%
 
o1318819.6%
 
B1309519.5%
 
n1309519.5%
 
a3020.4%
 
l2830.4%
 
R1900.3%
 
g1900.3%
 
r1900.3%
 
M740.1%
 
N19< 0.1%
 
p19< 0.1%
 
i19< 0.1%
 
c19< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII67272100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
u1328519.7%
 
e1328519.7%
 
o1318819.6%
 
B1309519.5%
 
n1309519.5%
 
a3020.4%
 
l2830.4%
 
R1900.3%
 
g1900.3%
 
r1900.3%
 
M740.1%
 
N19< 0.1%
 
19< 0.1%
 
p19< 0.1%
 
i19< 0.1%
 
c19< 0.1%
 

p160
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
13220 
Si
 
139
No aplica
 
19
ValueCountFrequency (%) 
No1322098.8%
 
Si1391.0%
 
No aplica190.1%
 
2020-12-12T19:52:24.237954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.286996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:24.342544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.009941695
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1323949.2%
 
o1323949.2%
 
i1580.6%
 
S1390.5%
 
a380.1%
 
190.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1349250.2%
 
Uppercase Letter1337849.8%
 
Space Separator190.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1323999.0%
 
S1391.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1323998.1%
 
i1581.2%
 
a380.3%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2687099.9%
 
Common190.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1323949.3%
 
o1323949.3%
 
i1580.6%
 
S1390.5%
 
a380.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26889100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1323949.2%
 
o1323949.2%
 
i1580.6%
 
S1390.5%
 
a380.1%
 
190.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

p161
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
13252 
Si
 
107
No aplica
 
19
ValueCountFrequency (%) 
No1325299.1%
 
Si1070.8%
 
No aplica190.1%
 
2020-12-12T19:52:24.418109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.467652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:24.523199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.009941695
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1327149.4%
 
o1327149.4%
 
i1260.5%
 
S1070.4%
 
a380.1%
 
190.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1349250.2%
 
Uppercase Letter1337849.8%
 
Space Separator190.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1327199.2%
 
S1070.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1327198.4%
 
i1260.9%
 
a380.3%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2687099.9%
 
Common190.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1327149.4%
 
o1327149.4%
 
i1260.5%
 
S1070.4%
 
a380.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26889100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1327149.4%
 
o1327149.4%
 
i1260.5%
 
S1070.4%
 
a380.1%
 
190.1%
 
p190.1%
 
l190.1%
 
c190.1%
 

p162
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
13197 
No
 
181
ValueCountFrequency (%) 
Si1319798.6%
 
No1811.4%
 
2020-12-12T19:52:24.596262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.640300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:24.687340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S1319749.3%
 
i1319749.3%
 
N1810.7%
 
o1810.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1319798.6%
 
N1811.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1319798.6%
 
o1811.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1319749.3%
 
i1319749.3%
 
N1810.7%
 
o1810.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S1319749.3%
 
i1319749.3%
 
N1810.7%
 
o1810.7%
 

p163
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
12810 
Regular
 
249
No aplica
 
181
Malo
 
138
ValueCountFrequency (%) 
Bueno1281095.8%
 
Regular2491.9%
 
No aplica1811.4%
 
Malo1381.0%
 
2020-12-12T19:52:24.757901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.804441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:24.860990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.081028554
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1312919.3%
 
u1305919.2%
 
e1305919.2%
 
B1281018.8%
 
n1281018.8%
 
a7491.1%
 
l5680.8%
 
R2490.4%
 
g2490.4%
 
r2490.4%
 
N1810.3%
 
1810.3%
 
p1810.3%
 
i1810.3%
 
c1810.3%
 
M1380.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5441580.1%
 
Uppercase Letter1337819.7%
 
Space Separator1810.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1281095.8%
 
R2491.9%
 
N1811.4%
 
M1381.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1312924.1%
 
u1305924.0%
 
e1305924.0%
 
n1281023.5%
 
a7491.4%
 
l5681.0%
 
g2490.5%
 
r2490.5%
 
p1810.3%
 
i1810.3%
 
c1810.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
181100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6779399.7%
 
Common1810.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1312919.4%
 
u1305919.3%
 
e1305919.3%
 
B1281018.9%
 
n1281018.9%
 
a7491.1%
 
l5680.8%
 
R2490.4%
 
g2490.4%
 
r2490.4%
 
N1810.3%
 
p1810.3%
 
i1810.3%
 
c1810.3%
 
M1380.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
181100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII67974100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1312919.3%
 
u1305919.2%
 
e1305919.2%
 
B1281018.8%
 
n1281018.8%
 
a7491.1%
 
l5680.8%
 
R2490.4%
 
g2490.4%
 
r2490.4%
 
N1810.3%
 
1810.3%
 
p1810.3%
 
i1810.3%
 
c1810.3%
 
M1380.2%
 

p164
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
12936 
Si
 
261
No aplica
 
181
ValueCountFrequency (%) 
No1293696.7%
 
Si2612.0%
 
No aplica1811.4%
 
2020-12-12T19:52:24.935554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:24.984096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.038142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.094707729
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1311746.8%
 
o1311746.8%
 
i4421.6%
 
a3621.3%
 
S2610.9%
 
1810.6%
 
p1810.6%
 
l1810.6%
 
c1810.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1446451.6%
 
Uppercase Letter1337847.7%
 
Space Separator1810.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1311798.0%
 
S2612.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1311790.7%
 
i4423.1%
 
a3622.5%
 
p1811.3%
 
l1811.3%
 
c1811.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
181100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2784299.4%
 
Common1810.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1311747.1%
 
o1311747.1%
 
i4421.6%
 
a3621.3%
 
S2610.9%
 
p1810.7%
 
l1810.7%
 
c1810.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
181100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII28023100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1311746.8%
 
o1311746.8%
 
i4421.6%
 
a3621.3%
 
S2610.9%
 
1810.6%
 
p1810.6%
 
l1810.6%
 
c1810.6%
 

p165
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
12802 
No
 
576
ValueCountFrequency (%) 
Si1280295.7%
 
No5764.3%
 
2020-12-12T19:52:25.109704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:25.154242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.201283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S1280247.8%
 
i1280247.8%
 
N5762.2%
 
o5762.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1280295.7%
 
N5764.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1280295.7%
 
o5764.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1280247.8%
 
i1280247.8%
 
N5762.2%
 
o5762.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S1280247.8%
 
i1280247.8%
 
N5762.2%
 
o5762.2%
 

p166
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
12363 
No aplica
 
576
Regular
 
355
Malo
 
84
ValueCountFrequency (%) 
Bueno1236392.4%
 
No aplica5764.3%
 
Regular3552.7%
 
Malo840.6%
 
2020-12-12T19:52:25.270843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:25.319385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.375933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.219016295
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1302318.7%
 
u1271818.2%
 
e1271818.2%
 
B1236317.7%
 
n1236317.7%
 
a15912.3%
 
l10151.5%
 
N5760.8%
 
5760.8%
 
p5760.8%
 
i5760.8%
 
c5760.8%
 
R3550.5%
 
g3550.5%
 
r3550.5%
 
M840.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5586680.0%
 
Uppercase Letter1337819.2%
 
Space Separator5760.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1236392.4%
 
N5764.3%
 
R3552.7%
 
M840.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1302323.3%
 
u1271822.8%
 
e1271822.8%
 
n1236322.1%
 
a15912.8%
 
l10151.8%
 
p5761.0%
 
i5761.0%
 
c5761.0%
 
g3550.6%
 
r3550.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
576100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6924499.2%
 
Common5760.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1302318.8%
 
u1271818.4%
 
e1271818.4%
 
B1236317.9%
 
n1236317.9%
 
a15912.3%
 
l10151.5%
 
N5760.8%
 
p5760.8%
 
i5760.8%
 
c5760.8%
 
R3550.5%
 
g3550.5%
 
r3550.5%
 
M840.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
576100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69820100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1302318.7%
 
u1271818.2%
 
e1271818.2%
 
B1236317.7%
 
n1236317.7%
 
a15912.3%
 
l10151.5%
 
N5760.8%
 
5760.8%
 
p5760.8%
 
i5760.8%
 
c5760.8%
 
R3550.5%
 
g3550.5%
 
r3550.5%
 
M840.1%
 

p167
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
11834 
No
1544 
ValueCountFrequency (%) 
Si1183488.5%
 
No154411.5%
 
2020-12-12T19:52:25.444993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:25.489030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.537072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S1183444.2%
 
i1183444.2%
 
N15445.8%
 
o15445.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1183488.5%
 
N154411.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1183488.5%
 
o154411.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1183444.2%
 
i1183444.2%
 
N15445.8%
 
o15445.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S1183444.2%
 
i1183444.2%
 
N15445.8%
 
o15445.8%
 

p168
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
11379 
No aplica
1544 
Regular
 
326
Malo
 
129
ValueCountFrequency (%) 
Bueno1137985.1%
 
No aplica154411.5%
 
Regular3262.4%
 
Malo1291.0%
 
2020-12-12T19:52:25.606131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:25.651170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.707719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.500747496
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1305217.7%
 
u1170515.9%
 
e1170515.9%
 
B1137915.5%
 
n1137915.5%
 
a35434.8%
 
l19992.7%
 
N15442.1%
 
15442.1%
 
p15442.1%
 
i15442.1%
 
c15442.1%
 
R3260.4%
 
g3260.4%
 
r3260.4%
 
M1290.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5866779.7%
 
Uppercase Letter1337818.2%
 
Space Separator15442.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1137985.1%
 
N154411.5%
 
R3262.4%
 
M1291.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1305222.2%
 
u1170520.0%
 
e1170520.0%
 
n1137919.4%
 
a35436.0%
 
l19993.4%
 
p15442.6%
 
i15442.6%
 
c15442.6%
 
g3260.6%
 
r3260.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7204597.9%
 
Common15442.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1305218.1%
 
u1170516.2%
 
e1170516.2%
 
B1137915.8%
 
n1137915.8%
 
a35434.9%
 
l19992.8%
 
N15442.1%
 
p15442.1%
 
i15442.1%
 
c15442.1%
 
R3260.5%
 
g3260.5%
 
r3260.5%
 
M1290.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII73589100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1305217.7%
 
u1170515.9%
 
e1170515.9%
 
B1137915.5%
 
n1137915.5%
 
a35434.8%
 
l19992.7%
 
N15442.1%
 
15442.1%
 
p15442.1%
 
i15442.1%
 
c15442.1%
 
R3260.4%
 
g3260.4%
 
r3260.4%
 
M1290.2%
 

p169
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
11338 
No aplica
1544 
Si
 
496
ValueCountFrequency (%) 
No1133884.8%
 
No aplica154411.5%
 
Si4963.7%
 
2020-12-12T19:52:25.778780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:25.825320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:25.879867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.807893557
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1288234.3%
 
o1288234.3%
 
a30888.2%
 
i20405.4%
 
15444.1%
 
p15444.1%
 
l15444.1%
 
c15444.1%
 
S4961.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2264260.3%
 
Uppercase Letter1337835.6%
 
Space Separator15444.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1288296.3%
 
S4963.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1288256.9%
 
a308813.6%
 
i20409.0%
 
p15446.8%
 
l15446.8%
 
c15446.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3602095.9%
 
Common15444.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1288235.8%
 
o1288235.8%
 
a30888.6%
 
i20405.7%
 
p15444.3%
 
l15444.3%
 
c15444.3%
 
S4961.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37564100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1288234.3%
 
o1288234.3%
 
a30888.2%
 
i20405.4%
 
15444.1%
 
p15444.1%
 
l15444.1%
 
c15444.1%
 
S4961.3%
 

p170
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
11712 
No aplica
1544 
Si
 
122
ValueCountFrequency (%) 
No1171287.5%
 
No aplica154411.5%
 
Si1220.9%
 
2020-12-12T19:52:25.955932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.003974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.057520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.807893557
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1325635.3%
 
o1325635.3%
 
a30888.2%
 
i16664.4%
 
15444.1%
 
p15444.1%
 
l15444.1%
 
c15444.1%
 
S1220.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2264260.3%
 
Uppercase Letter1337835.6%
 
Space Separator15444.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1325699.1%
 
S1220.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1325658.5%
 
a308813.6%
 
i16667.4%
 
p15446.8%
 
l15446.8%
 
c15446.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3602095.9%
 
Common15444.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1325636.8%
 
o1325636.8%
 
a30888.6%
 
i16664.6%
 
p15444.3%
 
l15444.3%
 
c15444.3%
 
S1220.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
1544100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37564100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1325635.3%
 
o1325635.3%
 
a30888.2%
 
i16664.4%
 
15444.1%
 
p15444.1%
 
l15444.1%
 
c15444.1%
 
S1220.3%
 

p171
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
9423 
No
3955 
ValueCountFrequency (%) 
Si942370.4%
 
No395529.6%
 
2020-12-12T19:52:26.129582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.173119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.220660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S942335.2%
 
i942335.2%
 
N395514.8%
 
o395514.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S942370.4%
 
N395529.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i942370.4%
 
o395529.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S942335.2%
 
i942335.2%
 
N395514.8%
 
o395514.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S942335.2%
 
i942335.2%
 
N395514.8%
 
o395514.8%
 

p172
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
9284 
No aplica
3955 
Regular
 
115
Malo
 
24
ValueCountFrequency (%) 
Bueno928469.4%
 
No aplica395529.6%
 
Regular1150.9%
 
Malo240.2%
 
2020-12-12T19:52:26.290220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.337260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.391807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length6.197936911
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1326316.0%
 
u939911.3%
 
e939911.3%
 
B928411.2%
 
n928411.2%
 
a80499.7%
 
l40944.9%
 
N39554.8%
 
39554.8%
 
p39554.8%
 
i39554.8%
 
c39554.8%
 
R1150.1%
 
g1150.1%
 
r1150.1%
 
M24< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6558379.1%
 
Uppercase Letter1337816.1%
 
Space Separator39554.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B928469.4%
 
N395529.6%
 
R1150.9%
 
M240.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1326320.2%
 
u939914.3%
 
e939914.3%
 
n928414.2%
 
a804912.3%
 
l40946.2%
 
p39556.0%
 
i39556.0%
 
c39556.0%
 
g1150.2%
 
r1150.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3955100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7896195.2%
 
Common39554.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1326316.8%
 
u939911.9%
 
e939911.9%
 
B928411.8%
 
n928411.8%
 
a804910.2%
 
l40945.2%
 
N39555.0%
 
p39555.0%
 
i39555.0%
 
c39555.0%
 
R1150.1%
 
g1150.1%
 
r1150.1%
 
M24< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
3955100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII82916100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1326316.0%
 
u939911.3%
 
e939911.3%
 
B928411.2%
 
n928411.2%
 
a80499.7%
 
l40944.9%
 
N39554.8%
 
39554.8%
 
p39554.8%
 
i39554.8%
 
c39554.8%
 
R1150.1%
 
g1150.1%
 
r1150.1%
 
M24< 0.1%
 

p173
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
9199 
No aplica
3955 
Si
 
224
ValueCountFrequency (%) 
No919968.8%
 
No aplica395529.6%
 
Si2241.7%
 
2020-12-12T19:52:26.458365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.501401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.554447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length4.069442368
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1315424.2%
 
o1315424.2%
 
a791014.5%
 
i41797.7%
 
39557.3%
 
p39557.3%
 
l39557.3%
 
c39557.3%
 
S2240.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3710868.2%
 
Uppercase Letter1337824.6%
 
Space Separator39557.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1315498.3%
 
S2241.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1315435.4%
 
a791021.3%
 
i417911.3%
 
p395510.7%
 
l395510.7%
 
c395510.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3955100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5048692.7%
 
Common39557.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1315426.1%
 
o1315426.1%
 
a791015.7%
 
i41798.3%
 
p39557.8%
 
l39557.8%
 
c39557.8%
 
S2240.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
3955100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54441100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1315424.2%
 
o1315424.2%
 
a791014.5%
 
i41797.7%
 
39557.3%
 
p39557.3%
 
l39557.3%
 
c39557.3%
 
S2240.4%
 

p174
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
13167 
No
 
211
ValueCountFrequency (%) 
Si1316798.4%
 
No2111.6%
 
2020-12-12T19:52:26.624508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.668045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.715086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S1316749.2%
 
i1316749.2%
 
N2110.8%
 
o2110.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1316798.4%
 
N2111.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1316798.4%
 
o2111.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1316749.2%
 
i1316749.2%
 
N2110.8%
 
o2110.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S1316749.2%
 
i1316749.2%
 
N2110.8%
 
o2110.8%
 

p175
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
12161 
Regular
 
765
Malo
 
241
No aplica
 
211
ValueCountFrequency (%) 
Bueno1216190.9%
 
Regular7655.7%
 
Malo2411.8%
 
No aplica2111.6%
 
2020-12-12T19:52:26.785146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:26.831686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:26.889235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.159440873
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
u1292618.7%
 
e1292618.7%
 
o1261318.3%
 
B1216117.6%
 
n1216117.6%
 
a14282.1%
 
l12171.8%
 
R7651.1%
 
g7651.1%
 
r7651.1%
 
M2410.3%
 
N2110.3%
 
2110.3%
 
p2110.3%
 
i2110.3%
 
c2110.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5543480.3%
 
Uppercase Letter1337819.4%
 
Space Separator2110.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1216190.9%
 
R7655.7%
 
M2411.8%
 
N2111.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u1292623.3%
 
e1292623.3%
 
o1261322.8%
 
n1216121.9%
 
a14282.6%
 
l12172.2%
 
g7651.4%
 
r7651.4%
 
p2110.4%
 
i2110.4%
 
c2110.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
211100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6881299.7%
 
Common2110.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u1292618.8%
 
e1292618.8%
 
o1261318.3%
 
B1216117.7%
 
n1216117.7%
 
a14282.1%
 
l12171.8%
 
R7651.1%
 
g7651.1%
 
r7651.1%
 
M2410.4%
 
N2110.3%
 
p2110.3%
 
i2110.3%
 
c2110.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
211100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69023100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
u1292618.7%
 
e1292618.7%
 
o1261318.3%
 
B1216117.6%
 
n1216117.6%
 
a14282.1%
 
l12171.8%
 
R7651.1%
 
g7651.1%
 
r7651.1%
 
M2410.3%
 
N2110.3%
 
2110.3%
 
p2110.3%
 
i2110.3%
 
c2110.3%
 

p176
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
10504 
Si
2874 
ValueCountFrequency (%) 
No1050478.5%
 
Si287421.5%
 
2020-12-12T19:52:26.961298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.007337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.054378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1050439.3%
 
o1050439.3%
 
S287410.7%
 
i287410.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1050478.5%
 
S287421.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1050478.5%
 
i287421.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1050439.3%
 
o1050439.3%
 
S287410.7%
 
i287410.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1050439.3%
 
o1050439.3%
 
S287410.7%
 
i287410.7%
 

p177
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No aplica
10504 
Bueno
2705 
Regular
 
143
Malo
 
26
ValueCountFrequency (%) 
No aplica1050478.5%
 
Bueno270520.2%
 
Regular1431.1%
 
Malo260.2%
 
2020-12-12T19:52:27.125439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.172479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.228027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.160113619
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a2117719.4%
 
o1323512.1%
 
l106739.8%
 
N105049.6%
 
105049.6%
 
p105049.6%
 
i105049.6%
 
c105049.6%
 
u28482.6%
 
e28482.6%
 
B27052.5%
 
n27052.5%
 
R1430.1%
 
g1430.1%
 
r1430.1%
 
M26< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8528478.1%
 
Uppercase Letter1337812.3%
 
Space Separator105049.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1050478.5%
 
B270520.2%
 
R1431.1%
 
M260.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a2117724.8%
 
o1323515.5%
 
l1067312.5%
 
p1050412.3%
 
i1050412.3%
 
c1050412.3%
 
u28483.3%
 
e28483.3%
 
n27053.2%
 
g1430.2%
 
r1430.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10504100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin9866290.4%
 
Common105049.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a2117721.5%
 
o1323513.4%
 
l1067310.8%
 
N1050410.6%
 
p1050410.6%
 
i1050410.6%
 
c1050410.6%
 
u28482.9%
 
e28482.9%
 
B27052.7%
 
n27052.7%
 
R1430.1%
 
g1430.1%
 
r1430.1%
 
M26< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
10504100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII109166100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a2117719.4%
 
o1323512.1%
 
l106739.8%
 
N105049.6%
 
105049.6%
 
p105049.6%
 
i105049.6%
 
c105049.6%
 
u28482.6%
 
e28482.6%
 
B27052.5%
 
n27052.5%
 
R1430.1%
 
g1430.1%
 
r1430.1%
 
M26< 0.1%
 

p178
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Si
7290 
No
6088 
ValueCountFrequency (%) 
Si729054.5%
 
No608845.5%
 
2020-12-12T19:52:27.296586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.340623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.387164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S729027.2%
 
i729027.2%
 
N608822.8%
 
o608822.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1337850.0%
 
Lowercase Letter1337850.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S729054.5%
 
N608845.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i729054.5%
 
o608845.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin26756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S729027.2%
 
i729027.2%
 
N608822.8%
 
o608822.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S729027.2%
 
i729027.2%
 
N608822.8%
 
o608822.8%
 

p179
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Bueno
6323 
No aplica
6088 
Regular
751 
Malo
 
216
ValueCountFrequency (%) 
Bueno632347.3%
 
No aplica608845.5%
 
Regular7515.6%
 
Malo2161.6%
 
2020-12-12T19:52:27.460227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.509769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.566318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length6.91642996
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1314314.2%
 
o1262713.6%
 
u70747.6%
 
e70747.6%
 
l70557.6%
 
B63236.8%
 
n63236.8%
 
N60886.6%
 
60886.6%
 
p60886.6%
 
i60886.6%
 
c60886.6%
 
R7510.8%
 
g7510.8%
 
r7510.8%
 
M2160.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7306279.0%
 
Uppercase Letter1337814.5%
 
Space Separator60886.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B632347.3%
 
N608845.5%
 
R7515.6%
 
M2161.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1314318.0%
 
o1262717.3%
 
u70749.7%
 
e70749.7%
 
l70559.7%
 
n63238.7%
 
p60888.3%
 
i60888.3%
 
c60888.3%
 
g7511.0%
 
r7511.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8644093.4%
 
Common60886.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1314315.2%
 
o1262714.6%
 
u70748.2%
 
e70748.2%
 
l70558.2%
 
B63237.3%
 
n63237.3%
 
N60887.0%
 
p60887.0%
 
i60887.0%
 
c60887.0%
 
R7510.9%
 
g7510.9%
 
r7510.9%
 
M2160.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII92528100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1314314.2%
 
o1262713.6%
 
u70747.6%
 
e70747.6%
 
l70557.6%
 
B63236.8%
 
n63236.8%
 
N60886.6%
 
60886.6%
 
p60886.6%
 
i60886.6%
 
c60886.6%
 
R7510.8%
 
g7510.8%
 
r7510.8%
 
M2160.2%
 

p180
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
7108 
No aplica
6088 
Si
 
182
ValueCountFrequency (%) 
No710853.1%
 
No aplica608845.5%
 
Si1821.4%
 
2020-12-12T19:52:27.640882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.690925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.745973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length5.18552848
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1319619.0%
 
o1319619.0%
 
a1217617.6%
 
i62709.0%
 
60888.8%
 
p60888.8%
 
l60888.8%
 
c60888.8%
 
S1820.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4990671.9%
 
Uppercase Letter1337819.3%
 
Space Separator60888.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1319698.6%
 
S1821.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1319626.4%
 
a1217624.4%
 
i627012.6%
 
p608812.2%
 
l608812.2%
 
c608812.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6328491.2%
 
Common60888.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1319620.9%
 
o1319620.9%
 
a1217619.2%
 
i62709.9%
 
p60889.6%
 
l60889.6%
 
c60889.6%
 
S1820.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69372100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1319619.0%
 
o1319619.0%
 
a1217617.6%
 
i62709.0%
 
60888.8%
 
p60888.8%
 
l60888.8%
 
c60888.8%
 
S1820.3%
 

p181
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
No
7261 
No aplica
6088 
Si
 
29
ValueCountFrequency (%) 
No726154.3%
 
No aplica608845.5%
 
Si290.2%
 
2020-12-12T19:52:27.822539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:52:27.872582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:27.927629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length5.18552848
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N1334919.2%
 
o1334919.2%
 
a1217617.6%
 
i61178.8%
 
60888.8%
 
p60888.8%
 
l60888.8%
 
c60888.8%
 
S29< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4990671.9%
 
Uppercase Letter1337819.3%
 
Space Separator60888.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1334999.8%
 
S290.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1334926.7%
 
a1217624.4%
 
i611712.3%
 
p608812.2%
 
l608812.2%
 
c608812.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6328491.2%
 
Common60888.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N1334921.1%
 
o1334921.1%
 
a1217619.2%
 
i61179.7%
 
p60889.6%
 
l60889.6%
 
c60889.6%
 
S29< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
6088100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69372100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N1334919.2%
 
o1334919.2%
 
a1217617.6%
 
i61178.8%
 
60888.8%
 
p60888.8%
 
l60888.8%
 
c60888.8%
 
S29< 0.1%
 

p182
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
Ninguno
13014 
Inundaciones
 
201
Falla geológica
 
60
Hundimiento del terreno
 
53
Deslizamiento
 
43
Other values (2)
 
7
ValueCountFrequency (%) 
Ninguno1301497.3%
 
Inundaciones2011.5%
 
Falla geológica600.4%
 
Hundimiento del terreno530.4%
 
Deslizamiento430.3%
 
Avalancha6< 0.1%
 
No sabe1< 0.1%
 
2020-12-12T19:52:28.003194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T19:52:28.052236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:28.125799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length7
Mean length7.19457318
Min length7

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2683927.9%
 
i1346714.0%
 
o1342513.9%
 
u1326813.8%
 
g1313413.6%
 
N1301513.5%
 
e5600.6%
 
a4430.5%
 
d3070.3%
 
l2820.3%
 
c2670.3%
 
s2450.3%
 
I2010.2%
 
1670.2%
 
t1490.2%
 
r1060.1%
 
m960.1%
 
F600.1%
 
ó600.1%
 
H530.1%
 
D43< 0.1%
 
z43< 0.1%
 
A6< 0.1%
 
v6< 0.1%
 
h6< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8270485.9%
 
Uppercase Letter1337813.9%
 
Space Separator1670.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1301597.3%
 
I2011.5%
 
F600.4%
 
H530.4%
 
D430.3%
 
A6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2683932.5%
 
i1346716.3%
 
o1342516.2%
 
u1326816.0%
 
g1313415.9%
 
e5600.7%
 
a4430.5%
 
d3070.4%
 
l2820.3%
 
c2670.3%
 
s2450.3%
 
t1490.2%
 
r1060.1%
 
m960.1%
 
ó600.1%
 
z430.1%
 
v6< 0.1%
 
h6< 0.1%
 
b1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
167100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin9608299.8%
 
Common1670.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2683927.9%
 
i1346714.0%
 
o1342514.0%
 
u1326813.8%
 
g1313413.7%
 
N1301513.5%
 
e5600.6%
 
a4430.5%
 
d3070.3%
 
l2820.3%
 
c2670.3%
 
s2450.3%
 
I2010.2%
 
t1490.2%
 
r1060.1%
 
m960.1%
 
F600.1%
 
ó600.1%
 
H530.1%
 
D43< 0.1%
 
z43< 0.1%
 
A6< 0.1%
 
v6< 0.1%
 
h6< 0.1%
 
b1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
167100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9618999.9%
 
None600.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2683927.9%
 
i1346714.0%
 
o1342514.0%
 
u1326813.8%
 
g1313413.7%
 
N1301513.5%
 
e5600.6%
 
a4430.5%
 
d3070.3%
 
l2820.3%
 
c2670.3%
 
s2450.3%
 
I2010.2%
 
1670.2%
 
t1490.2%
 
r1060.1%
 
m960.1%
 
F600.1%
 
H530.1%
 
D43< 0.1%
 
z43< 0.1%
 
A6< 0.1%
 
v6< 0.1%
 
h6< 0.1%
 
b1< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó60100.0%
 

p183
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.6 KiB
La recogen los servicios de aseo
11790 
La llevan a contenedor_ basurero público
1468 
La recoge un servicio informal (Zorra_ carreta_ etc)
 
84
La queman
 
16
La entregan a un reciclador
 
8
Other values (5)
 
12
ValueCountFrequency (%) 
La recogen los servicios de aseo1179088.1%
 
La llevan a contenedor_ basurero público146811.0%
 
La recoge un servicio informal (Zorra_ carreta_ etc)840.6%
 
La queman160.1%
 
La entregan a un reciclador80.1%
 
La reutilizan4< 0.1%
 
No responde4< 0.1%
 
La tiran al patio_ lote_ zanja o baldío o vía pública2< 0.1%
 
La tiran al río_ caño_ quebrada o laguna1< 0.1%
 
La comercializan1< 0.1%
 
2020-12-12T19:52:28.204367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-12-12T19:52:28.260416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:28.355497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length32
Mean length32.9635222
Min length9

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
6696215.2%
 
e6529814.8%
 
o5339812.1%
 
s4871611.0%
 
a299736.8%
 
r286106.5%
 
c268736.1%
 
i253255.7%
 
n164173.7%
 
l163013.7%
 
L133743.0%
 
v133443.0%
 
d132733.0%
 
g118832.7%
 
b29410.7%
 
t16550.4%
 
_16420.4%
 
u15820.4%
 
p14760.3%
 
ú14700.3%
 
m101< 0.1%
 
f84< 0.1%
 
(84< 0.1%
 
Z84< 0.1%
 
)84< 0.1%
 
Other values (6)36< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter35875281.4%
 
Space Separator6696215.2%
 
Uppercase Letter134623.1%
 
Connector Punctuation16420.4%
 
Open Punctuation84< 0.1%
 
Close Punctuation84< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L1337499.3%
 
Z840.6%
 
N4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e6529818.2%
 
o5339814.9%
 
s4871613.6%
 
a299738.4%
 
r286108.0%
 
c268737.5%
 
i253257.1%
 
n164174.6%
 
l163014.5%
 
v133443.7%
 
d132733.7%
 
g118833.3%
 
b29410.8%
 
t16550.5%
 
u15820.4%
 
p14760.4%
 
ú14700.4%
 
m101< 0.1%
 
f84< 0.1%
 
q17< 0.1%
 
z7< 0.1%
 
í5< 0.1%
 
j2< 0.1%
 
ñ1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
66962100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_1642100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(84100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)84100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin37221484.4%
 
Common6877215.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e6529817.5%
 
o5339814.3%
 
s4871613.1%
 
a299738.1%
 
r286107.7%
 
c268737.2%
 
i253256.8%
 
n164174.4%
 
l163014.4%
 
L133743.6%
 
v133443.6%
 
d132733.6%
 
g118833.2%
 
b29410.8%
 
t16550.4%
 
u15820.4%
 
p14760.4%
 
ú14700.4%
 
m101< 0.1%
 
f84< 0.1%
 
Z84< 0.1%
 
q17< 0.1%
 
z7< 0.1%
 
í5< 0.1%
 
N4< 0.1%
 
Other values (2)3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
6696297.4%
 
_16422.4%
 
(840.1%
 
)840.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII43951099.7%
 
None14760.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
6696215.2%
 
e6529814.9%
 
o5339812.1%
 
s4871611.1%
 
a299736.8%
 
r286106.5%
 
c268736.1%
 
i253255.8%
 
n164173.7%
 
l163013.7%
 
L133743.0%
 
v133443.0%
 
d132733.0%
 
g118832.7%
 
b29410.7%
 
t16550.4%
 
_16420.4%
 
u15820.4%
 
p14760.3%
 
m101< 0.1%
 
f84< 0.1%
 
(84< 0.1%
 
Z84< 0.1%
 
)84< 0.1%
 
q17< 0.1%
 
Other values (3)13< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ú147099.6%
 
í50.3%
 
ñ10.1%
 

icvmd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5180
Distinct (%)39.5%
Missing268
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean47.56665828
Minimum11.3
Maximum89.19
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:28.435066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile26.25
Q136.4325
median44.915
Q357.02
95-th percentile75.94
Maximum89.19
Range77.89
Interquartile range (IQR)20.5875

Descriptive statistics

Standard deviation15.14258008
Coefficient of variation (CV)0.3183444166
Kurtosis-0.5122401363
Mean47.56665828
Median Absolute Deviation (MAD)9.62
Skewness0.4992975428
Sum623598.89
Variance229.2977315
MonotocityNot monotonic
2020-12-12T19:52:28.522641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
38.15110.1%
 
37.03110.1%
 
36.81110.1%
 
40.95110.1%
 
45.75110.1%
 
41.39100.1%
 
38.61100.1%
 
42.62100.1%
 
36.1790.1%
 
37.790.1%
 
37.8990.1%
 
32.9890.1%
 
38.8590.1%
 
33.7490.1%
 
41.4690.1%
 
37.690.1%
 
46.4990.1%
 
37.5290.1%
 
39.4190.1%
 
37.7290.1%
 
44.3890.1%
 
46.4290.1%
 
42.4190.1%
 
39.3790.1%
 
37.4990.1%
 
Other values (5155)1287296.2%
 
(Missing)2682.0%
 
ValueCountFrequency (%) 
11.31< 0.1%
 
12.521< 0.1%
 
13.921< 0.1%
 
14.061< 0.1%
 
14.431< 0.1%
 
14.651< 0.1%
 
15.221< 0.1%
 
15.261< 0.1%
 
15.371< 0.1%
 
15.441< 0.1%
 
ValueCountFrequency (%) 
89.191< 0.1%
 
88.931< 0.1%
 
88.821< 0.1%
 
88.751< 0.1%
 
87.531< 0.1%
 
87.321< 0.1%
 
87.161< 0.1%
 
87.011< 0.1%
 
86.951< 0.1%
 
86.681< 0.1%
 

icv
Real number (ℝ≥0)

MISSING

Distinct3402
Distinct (%)25.9%
Missing251
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean73.45867982
Minimum30.32
Maximum96.3
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:28.615721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum30.32
5-th percentile59
Q168.54
median74.17
Q378.99
95-th percentile85.98
Maximum96.3
Range65.98
Interquartile range (IQR)10.45

Descriptive statistics

Standard deviation8.317211059
Coefficient of variation (CV)0.1132229858
Kurtosis0.9906044911
Mean73.45867982
Median Absolute Deviation (MAD)5.22
Skewness-0.6009032813
Sum964292.09
Variance69.1759998
MonotocityNot monotonic
2020-12-12T19:52:28.697291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
76270.2%
 
76.05260.2%
 
74.25240.2%
 
76.54240.2%
 
78.29230.2%
 
77.97230.2%
 
73.27220.2%
 
77.65220.2%
 
74.58210.2%
 
74.31210.2%
 
76.14200.1%
 
79.73200.1%
 
74.26200.1%
 
75.63190.1%
 
76.11190.1%
 
75.06190.1%
 
78.92190.1%
 
75.39180.1%
 
77.04180.1%
 
75.89180.1%
 
71.65180.1%
 
71.98180.1%
 
73.78180.1%
 
79.12180.1%
 
71.68180.1%
 
Other values (3377)1261494.3%
 
(Missing)2511.9%
 
ValueCountFrequency (%) 
30.321< 0.1%
 
32.181< 0.1%
 
32.221< 0.1%
 
32.361< 0.1%
 
32.841< 0.1%
 
35.351< 0.1%
 
35.381< 0.1%
 
35.471< 0.1%
 
35.491< 0.1%
 
35.91< 0.1%
 
ValueCountFrequency (%) 
96.31< 0.1%
 
95.51< 0.1%
 
95.331< 0.1%
 
95.171< 0.1%
 
95.041< 0.1%
 
94.561< 0.1%
 
94.531< 0.1%
 
94.451< 0.1%
 
94.431< 0.1%
 
94.291< 0.1%
 

fep2013
Real number (ℝ≥0)

Distinct665
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.51902324
Minimum1.5
Maximum169
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:28.786368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile32.11603255
Q142.666667
median50.610169
Q360.844444
95-th percentile78.129032
Maximum169
Range167.5
Interquartile range (IQR)18.177777

Descriptive statistics

Standard deviation14.78179292
Coefficient of variation (CV)0.2814559756
Kurtosis1.536831542
Mean52.51902324
Median Absolute Deviation (MAD)9.023164
Skewness0.7114801686
Sum702599.4929
Variance218.501402
MonotocityNot monotonic
2020-12-12T19:52:28.871942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
57.234848870.7%
 
53.728814860.6%
 
55860.6%
 
42.724138860.6%
 
45.253968820.6%
 
60.181818750.6%
 
70.074074730.5%
 
67.120879710.5%
 
62.380435690.5%
 
35.865591670.5%
 
42.666667670.5%
 
61.5670.5%
 
56.725275660.5%
 
49.1640.5%
 
40.211765630.5%
 
46.384615630.5%
 
70.515464620.5%
 
52.025974600.4%
 
53.021739590.4%
 
50.740741590.4%
 
44.826087580.4%
 
47.51049570.4%
 
42.253086570.4%
 
50.084337570.4%
 
58.433333570.4%
 
Other values (640)1168087.3%
 
ValueCountFrequency (%) 
1.53< 0.1%
 
4.51< 0.1%
 
5.45454570.1%
 
7.09090980.1%
 
8.1666675< 0.1%
 
10130.1%
 
111< 0.1%
 
131< 0.1%
 
13.058824120.1%
 
13.11764780.1%
 
ValueCountFrequency (%) 
1691< 0.1%
 
123.7272732< 0.1%
 
116.409091160.1%
 
109.3333331< 0.1%
 
107.232558250.2%
 
106.676471190.1%
 
1066< 0.1%
 
103.3333332< 0.1%
 
101.84615490.1%
 
99.78787990.1%
 

fevh2013
Real number (ℝ≥0)

Distinct83
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.97294068
Minimum5
Maximum85
Zeros0
Zeros (%)0.0%
Memory size104.6 KiB
2020-12-12T19:52:28.964521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile54.81592
Q155.822134
median56.29386
Q356.641026
95-th percentile57.038462
Maximum85
Range80
Interquartile range (IQR)0.818892

Descriptive statistics

Standard deviation2.962648644
Coefficient of variation (CV)0.05293001596
Kurtosis134.5461287
Mean55.97294068
Median Absolute Deviation (MAD)0.413457
Skewness-10.06572961
Sum748806.0005
Variance8.777286985
MonotocityNot monotonic
2020-12-12T19:52:29.050095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
56.7378825574.2%
 
56.1709095504.1%
 
56.1222885073.8%
 
55.8221345063.8%
 
56.381715033.8%
 
56.6357894753.6%
 
55.8095244623.5%
 
56.5618224613.4%
 
56.1814064413.3%
 
56.4834914243.2%
 
56.4285714203.1%
 
55.6696973302.5%
 
56.9027363292.5%
 
56.3161293102.3%
 
54.8831173082.3%
 
55.3660133062.3%
 
56.2338982952.2%
 
56.1473682852.1%
 
56.3274652842.1%
 
55.4522062722.0%
 
56.6417912682.0%
 
56.7744362662.0%
 
55.7406022662.0%
 
57.1137252551.9%
 
55.8976382541.9%
 
Other values (58)404430.2%
 
ValueCountFrequency (%) 
51< 0.1%
 
14.54< 0.1%
 
18.318182440.3%
 
21.2200.1%
 
342< 0.1%
 
40.254< 0.1%
 
474< 0.1%
 
47.428571140.1%
 
48.923077130.1%
 
50.1100.1%
 
ValueCountFrequency (%) 
851< 0.1%
 
83.1666676< 0.1%
 
821< 0.1%
 
61.76250.2%
 
61.3100.1%
 
60.33333390.1%
 
59.461538130.1%
 
58.959184490.4%
 
58.6666676< 0.1%
 
58.383562730.5%
 

Interactions

2020-12-12T19:52:04.299296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.393377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.485456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.576034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.671116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.763195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.852772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:04.943350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.035930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.130011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.221590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.316171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.408751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.499329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.584902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.670476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.763556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.852633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:05.939207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.026282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.115359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.207938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.297015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.388093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.483175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.576255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.666333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.755910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.850491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:06.941570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.030646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.118722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.210801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.303381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.393959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.486538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.579118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.677203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.773786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.867366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:07.967953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.064536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.160118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.258203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.356788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.456373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.554958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.655044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.757132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.850212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:08.939789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.027865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.123948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.215526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.308106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.400185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.493766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.589348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.682428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.776509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.870590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:09.961668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.050245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.138321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.232401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.321979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.410054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.499632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.589709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.686292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.777371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.869450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:10.961529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.052607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.141183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.229259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.322840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.414419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.503495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.592072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.682149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.776731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.865807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:11.957386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.050967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.146549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.243132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.337713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.437799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.533882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.628964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.723545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.820128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:12.918213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.012794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.109878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.206461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.305046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.400628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.496711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.597297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.694381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.790463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.887547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:13.985131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.084216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.179799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.277883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.376468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.471550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.564630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.653706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.749289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.844370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:14.937450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.029029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.123611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.218692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.310772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.404852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.501936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.597518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.690598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.783178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.881263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:15.975844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.067423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.159502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.258087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.354670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.449251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.545334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.641917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.736499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.831580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:16.925661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.025247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.122331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.218413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.314996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.411579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.512667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.608249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:17.704832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:52:29.144176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:52:29.296807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:52:29.448437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:52:29.641604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T19:52:29.886815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T19:52:18.113684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:19.490369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:19.884207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:52:20.019824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

anoformhogarp1p4p5p6p7p10p11p146p147p148p149p150p151p152p153p154p155p156p157p158p159p160p161p162p163p164p165p166p167p168p169p170p171p172p173p174p175p176p177p178p179p180p181p182p183icvmdicvfep2013fevh2013
02013533951MedellínUrbanoBELEN1016012.041CasaLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos11011318SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoNoNo aplicaNo aplicaSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo60.2879.9433.06000055.366013
12013475081MedellínUrbanoROBLEDO1007016.031ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos01011306SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa llevan a contenedor_ basurero público45.8568.5245.89552254.883117
22013425301NoMedellínUrbanoSANTA CRUZ1002005.022CasaLadrillo ranurado o revitadoCemento o gravillaEntidad prestadora de servicios públicos11010508SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiRegularNoNoNingunoLa recogen los servicios de aseo35.8268.4453.21428656.635789
32013491861NoMedellínUrbanoBUENOS AIRES1009011.031ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos10010305SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiRegularNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo54.3376.8549.62105355.822134
42013517551NoMedellínUrbanoEL POBLADO1014011.061ApartamentoLadrillo ranurado o revitadoMadera pulida y lacada_ parqué_ cristalEntidad prestadora de servicios públicos00112408SiBuenoSiNoSiRegularNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiMaloNoNoNingunoLa recogen los servicios de aseo69.6085.5352.02597456.737882
52013506711MedellínUrbanoLAURELES-ESTADIO1011014.041ApartamentoLadrillo ranurado o revitadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos00110305SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoNoNo aplicaNo aplicaSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo60.3878.11107.23255856.774436
62013458351NoMedellínUrbanoDOCE DE OCTUBRE1006007.021CasaLadrillo-bloque-adobe sin ranurar_ sin revocar o sin revitarCemento o gravillaEntidad prestadora de servicios públicos10010114SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseoNaN52.1555.13333356.122288
72013536991NoMedellínUrbanoBELEN1016017.031ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos01010305SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo50.5676.7157.23484856.561822
82013484361MedellínUrbanoVILLA HERMOSA1008013.021ApartamentoLadrillo-bloque-adobe sin ranurar_ sin revocar o sin revitarCemento o gravillaEntidad prestadora de servicios públicos00010012SiBuenoNoNoSiBuenoNoSiBuenoSiMaloNoNoNoNo aplicaNo aplicaSiBuenoSiBuenoNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseo34.5962.7544.11494355.669697
92013516991NoMedellínUrbanoEL POBLADO1014010.021CasaLadrillo-bloque-adobe revocado o o pintadoCemento o gravillaEntidad prestadora de servicios públicos01010204SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoNoNo aplicaNo aplicaSiBuenoSiBuenoSiBuenoNoNoNingunoLa recogen los servicios de aseo34.6958.7451.52459047.428571

Last rows

anoformhogarp1p4p5p6p7p10p11p146p147p148p149p150p151p152p153p154p155p156p157p158p159p160p161p162p163p164p165p166p167p168p169p170p171p172p173p174p175p176p177p178p179p180p181p182p183icvmdicvfep2013fevh2013
133682013475091NoMedellínUrbanoROBLEDO1007016.041ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos11011307SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiRegularNoNoNingunoLa recogen los servicios de aseo68.3284.2662.61111158.383562
133692013458121NoMedellínUrbanoDOCE DE OCTUBRE1006007.021ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos11010205SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiRegularNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseoNaNNaN56.49230856.122288
133702013493551MedellínUrbanoBUENOS AIRES1009013.021ApartamentoLadrillo ranurado o revitadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos00010304SiBuenoNoNoSiBuenoSiSiBuenoSiBuenoSiNoNoNo aplicaNo aplicaSiBuenoSiBuenoNoNo aplicaNo aplicaNo aplicaInundacionesLa recogen los servicios de aseo32.9467.9049.62105356.734597
133712013437771MedellínUrbanoARANJUEZ1004002.031CasaLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos10010316SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo50.3277.4851.90322656.428571
133722013459911MedellínUrbanoDOCE DE OCTUBRE1006011.011CasaLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos00110114SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoNoNo aplicaNo aplicaSiBuenoSiBuenoNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseo35.1867.9295.58181855.875000
133732013469761NoMedellínUrbanoROBLEDO1007007.041ApartamentoLadrillo-bloque-adobe revocado o o pintadoMármolEntidad prestadora de servicios públicos00111216SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo63.1280.0751.13333358.383562
133742013467171NoMedellínUrbanoSAN JAVIER1013016.021ApartamentoLadrillo ranurado o revitadoCemento o gravillaEntidad prestadora de servicios públicos00010102SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoNoNoNoNo aplicaNo aplicaSiBuenoNoNo aplicaNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseo30.1269.0946.88888956.902736
133752013429351MedellínUrbanoMANRIQUE1003001.021ApartamentoLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos11010205SiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaNo aplicaNo aplicaSiBuenoNoSiBuenoNoNo aplicaNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseo38.2470.5639.71875056.381710
133762013525551MedellínUrbanoBELEN1016001.051CasaLadrillo-bloque-adobe revocado o o pintadoBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos11011307SiBuenoNoNoSiBuenoNoSiBuenoSiBuenoSiNoSiBuenoNoSiBuenoNoNo aplicaSiBuenoNoNoNingunoLa recogen los servicios de aseo68.3285.5235.86559157.113725
133772013442991NoMedellínUrbanoARANJUEZ1004012.021ApartamentoLadrillo-bloque-adobe sin ranurar_ sin revocar o sin revitarBaldosa_ vinilo_ tableta o ladrilloEntidad prestadora de servicios públicos00010113SiBuenoNoNoSiBuenoNoSiBuenoNoNo aplicaNo aplicaNo aplicaSiBuenoNoSiBuenoNoNo aplicaNoNo aplicaNo aplicaNo aplicaNingunoLa recogen los servicios de aseo25.8860.4063.58139555.740602